Setting appropriate alarm levels for scalar vibration parameter data is important for automatically identifying potential issues arising with machines being monitored. If reliable alarm levels are established, the amount of time a vibration analyst needs to spend examining data on machines that do not exhibit any operational issues may be significantly reduced. Accordingly, the vibration analyst will be able to focus their valuable time on those machines that have potential issues arising. This is particularly important considering the decreasing number of skilled vibration analysts available to monitor large groups of machinery.
There are several statistical methods for analyzing scalar vibration parameter trend data, the most common being the calculation of a standard deviation. The issue with this approach is that for a reliable or meaningful standard deviation it is assumed that the data forms a normal or Gaussian distribution. However, most scalar vibration trend data fails to follow a normal distribution and therefore, although a standard deviation can be calculated from the data, it is not a reliable representation of the vibration trend data.
An example of a conventional technique for vibrational trend data is illustrated in FIG. 1. FIG. 1 illustrates predicted and actual motor vibrational velocity data in inches per sec (in/sec) for a machine motor. FIG. 1 shows the discrepancy between the actual scalar vibrational data distribution (Curve C) for a typical machine and what a normal distribution (Curve A) and a log normal distribution curve (Curve B) with the same standard distribution would look like.
In FIG. 1, Curve A has the same mean and standard deviation as that calculated from raw data. For example, point D on Curve A has a calculated mean value of 0.0512 in/sec and a calculated standard deviation of 0.0615 in/sec. Point D on Curve A has a mean plus standard deviation of 0.113 in/sec. The normal distribution of Curve A is very broad due to a number of very large measurement values (˜23 in/sec) in the distribution curve. The large values may be real values due to the operating environment, or may be “bad” measurements. Accordingly, the standard deviation is unlikely to be an accurate representation of the measurment values. “Bad” measurement values may be difficult to recognize using the normal distribution curve. Curve A is vastly different from Curve C and thus the mean and standard deviation do not represent the actual data in a meaningful way.
Likewise, a log normal distribution curve (Curve B) still does not represent the actual data very well. The log normal distribution curve (Curve B) minimizes the impact of large measurement values by uing the logrithm of the measured values to calculate the mean and standard deviation. Using this method, large measurement values are minimized. For example, Curve B has a calculated mean value of 0.0362 in/sec and a calculated standard deviation of 0.046 in/sec. Point E on Curve B has a log normal mean value plus standard deviation of 0.079 in/sec.
There are two measures for how well a normal distribution represents the actual data. They are: skewness, which is a measure of symmetry, or more precisely, the lack of symmetry. A distribution, or data set, is symmetric if it looks the same to the left and right of the center point. The skewness for a normal distribution is zero.
The other measure is kurtosis which is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. The kurtosis for a standard normal distribution is zero. Positive kurtosis indicates a “heavy-tailed” distribution and negative kurtosis indicates a “light tailed” distribution.
As can be seen in FIG. 1, neither the skewness nor the kurtosis are close to zero; i.e. for Curve A−skewness=7.06 and kurtosis=100.4 and for Curve B−skewness=0.36 and kurtosis=0.38. Skewness and kurtosis are useful for identifying whether or not the mean and standard deviations are a reliable or meaningful representation of the data under evaluation. In general vibrational data is not well represented by skewness and kurtosis as shown by the curves in FIG. 1. Accordingly, there remains a need for a more reliable system and method for setting alarm limit levels for vibrational data for machinery.
In view of the foregoing an embodiment of the disclosure provides a system for setting vibrational alarms for machinery. The system includes a vibrational alarm device having a plurality of vibration data inputs from a machinery group, a memory for storing historical vibration data from the machinery group, an accumulator for generating average vibrational data for the machinery group, a processor for selecting a vibration alarm limit based on a cumulative distribution curve of the average vibrational data, and a warning alarm to alert a user that the machinery has reached the vibrational alarm limit when vibration data from a machine in the machinery group reaches the vibrational alarm limit.
One embodiment of the disclosure provides a hand-held vibration monitor. The vibration monitor has a data input device for inputting vibration data to a central processing unit from a machinery group. The central processing unit has a vibration data storage module for storing the vibration data, a data processor for generating a cumulative distribution curve from the vibration data, and an output for providing an alarm limit. An alarm is provided for alerting a user when the alarm limit is reached by one or more machines in the machinery group.
Another embodiment of the disclosure provides a method for monitoring vibration on a group of machines. The method includes providing a system for setting vibrational alarms for each of the machines in the group of machines. The system includes a vibrational alarm device having a plurality of vibration data inputs from a machinery group, a memory for storing historical vibration data from the machinery group, an accumulator for generating average vibrational data for the machinery group, a processor for selecting a vibration alarm limit based on a cumulative distribution curve of the average vibrational data, and a warning alarm to alert a user that the machinery has reached the vibrational alarm limit when vibration data from a machine in the group of machines reaches the vibrational alarm limit. A user may accept the vibration alarm limit or select a new vibration alarm limit. Data is then input from a matching into the system to determine if the vibration alarm limit is reached.
Some embodiments of the disclosure provide a machinery group that contains machines of similar type, having similar size, located in similar environments, and performing similar functions.
Other embodiments of the disclosure provide that the accumulator is a computer for generating average vibrational data.
Still other embodiments of the disclosure provide that the warning alarm is an audible alarm device. Other embodiments of the disclosure provide that the warning alarm is a visual alarm display.
In some embodiments of the disclosure the system is a portable vibration monitor. In other embodiments of the disclosure vibrational data is input from a sensor attached to the machine.
Other embodiments of the disclosure provide that the vibrational alarm limit is selected from one or more of an advisory alarm limit, a warning alarm limit and a danger alarm limit.
In still other embodiments, the processor is an application specific integrated circuit (ASIC).
An advantage of the systems and methods described herein is that more reliable alarm limits may be established that more closely reflect actual machine vibrational characteristics than can be obtained with prior art curve fitting techniques.